The natural history and individualized prediction of liver stiffness-based fibrosis risk in metabolic dysfunction-associated steatotic liver disease
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND/AIMS: Liver stiffness measurement (LSM) is a key tool for risk stratification in metabolic dysfunction-associated steatotic liver disease (MASLD), yet static thresholds fail to capture dynamic transition across risk strata. We aimed to characterize LSM-risk transitions and develop a time-updated, individualized model for predicting state transitions, liver-related events and death (LREs/death). METHODS: In a real-world MASLD cohort, we applied a multi-state, time-homogeneous Markov model to quantify annual transition probabilities and mean state occupancy times across LSM-defined low-, intermediate-, and high-risk strata. A Markov model incorporating age, sex, type 2 diabetes (T2D), hypertension was used to generate individualized, time-updated risk trajectories and probabilities of LREs/death. RESULTS: Among 11,514 MASLD individuals with ≥2 vibration-controlled transient elastography assessments, the low-risk category demonstrated notable stability, with 92% remaining unchanged at 1 year and a mean occupancy time of 8.43 years (95% confidence interval [CI] 7.94-8.95). Contrarily, the intermediate-risk category was highly dynamic, with only 39% remaining unchanged after 1 year and a mean occupancy time of 0.92 years (95% CI 0.88-0.96). T2D, hypertension, and obesity substantially shorten low-risk occupancy time, whereas antidiabetic medication was associated with more favorable transitions. Finally, we developed a dynamic, multi-state Markov model integrating longitudinal LSM-defined risk states with relevant covariates to generate individualized predictions of state transitions and risks of LREs/death. CONCLUSIONS: LSM-based strata in MASLD represent distinct and meaningful dynamic trajectories. In particular, the marked instability of the intermediate-risk state supports more frequent reassessment. By quantifying transition pathways and time-updated risks of LREs/death, this model may inform the personalized surveillance intervals and risk-adapted management.
Authors: Yu Shi, Ruoqi Zhou, Seung Up Kim, Terry Cheuk-Fung Yip, Salvatore Petta, Elisabetta Bugianesi, Masato Yoneda, Manuel Romero-Gomez, Emmanuel Tsochatzis, Philip Newsome, Hannes Hagström, George Boon-Bee Goh, Wah-Kheong Chan, José-Luis Calleja, Jerome Boursier, Arun J. Sanyal, Jian-Gao Fan, Laurent Castera, Victor de Lédinghen, Michelle Lai, Xiao-Dong Zhou, Vincent Wai-Sun Wong, Ming-Hua Zheng, on behalf of VCTE-Prognosis Study Group
Institutions: First Affiliated Hospital Zhejiang University, State Key Laboratory of Diagnosis and Treatment of Infectious Diseases, Zhejiang University, Yonsei University, Yonsei University Health System, Chinese University of Hong Kong, University of Palermo, Azienda Ospedaliera Citta' della Salute e della Scienza di Torino, University of Turin, Yokohama City University, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, The Royal Free Hospital, University College London, King's College London, King's College Hospital, Karolinska University Hospital, Karolinska Institutet, Singapore General Hospital, University of Malaya, Hospital Universitario Puerta de Hierro Majadahonda, Centre Hospitalier Universitaire d'Angers, Virginia Commonwealth University, Virginia Commonwealth University Medical Center, Shanghai Jiao Tong University, Inserm, Université Paris Cité, Hôpital Beaujon, Assistance Publique – Hôpitaux de Paris, Centre de Recherche sur l'Inflammation, Echosens (France), Beth Israel Deaconess Medical Center, Harvard University, First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7161807885